code: def uniform(
      random_state,
      low=0.0,
      high=1.0,
      size=None,
      chunk_size=None,
      gpu=None,
      dtype=None,
  ):
      r"""
      Draw samples from a uniform distribution.
  
      Samples are uniformly distributed over the half-open interval
      ``[low, high)`` (includes low, but excludes high).  In other words,
      any value within the given interval is equally likely to be drawn
      by `uniform`.
  
      Parameters
      ----------
      low : float or array_like of floats, optional
          Lower boundary of the output interval.  All values generated will be
          greater than or equal to low.  The default value is 0.
      high : float or array_like of floats
          Upper boundary of the output interval.  All values generated will be
          less than high.  The default value is 1.0.
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  If size is ``None`` (default),
          a single value is returned if ``low`` and ``high`` are both scalars.
          Otherwise, ``mt.broadcast(low, high).size`` samples are drawn.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      out : Tensor or scalar
          Drawn samples from the parameterized uniform distribution.
  
      See Also
      --------
      randint : Discrete uniform distribution, yielding integers.
      random_integers : Discrete uniform distribution over the closed
                        interval ``[low, high]``.
      random_sample : Floats uniformly distributed over ``[0, 1)``.
      random : Alias for `random_sample`.
      rand : Convenience function that accepts dimensions as input, e.g.,
             ``rand(2,2)`` would generate a 2-by-2 array of floats,
             uniformly distributed over ``[0, 1)``.
  
      Notes
      -----
      The probability density function of the uniform distribution is
  
      .. math:: p(x) = \frac{1}{b - a}
  
      anywhere within the interval ``[a, b)``, and zero elsewhere.
  
      When ``high`` == ``low``, values of ``low`` will be returned.
      If ``high`` < ``low``, the results are officially undefined
      and may eventually raise an error, i.e. do not rely on this
      function to behave when passed arguments satisfying that
      inequality condition.
  
      Examples
      --------
      Draw samples from the distribution:
  
      >>> import mars.tensor as mt
  
      >>> s = mt.random.uniform(-1,0,1000)
  
      All values are within the given interval:
  
      >>> mt.all(s >= -1).execute()
      True
      >>> mt.all(s < 0).execute()
      True
  
      Display the histogram of the samples, along with the
      probability density function:
  
      >>> import matplotlib.pyplot as plt
      >>> count, bins, ignored = plt.hist(s.execute(), 15, normed=True)
      >>> plt.plot(bins, mt.ones_like(bins).execute(), linewidth=2, color='r')
      >>> plt.show()
      """
      if dtype is None:
          dtype = (
              np.random.RandomState()
              .uniform(handle_array(low), handle_array(high), size=(0,))
              .dtype
          )
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorUniform(size=size, seed=seed, gpu=gpu, dtype=dtype)
      return op(low, high, chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/uniform.py
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import AnyField
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, handle_array, TensorDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: uniform(random_state, low, high, size, chunk_size, gpu, dtype) and returns: op